electricsheepafrica/africa-ilo-ees-tees-sex-geo-dsb-nb-employees-by-sex-rural-urban-areas-and-disability

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 1K<n<10K tags: - tabular - africa - ilostat - employees - ilo - labour - employment pretty_name: "Employees by sex, rural / urban areas and disability status (thousands) | Africa (ILOSTAT)" --- # Employees by sex, rural / urban areas and disability status (thousands) | Africa (ILOSTAT) 🌍 **2,546 observations** · **35 Africa countries** · **2006–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **2,546 observations** of `Employees` data across **35 Africa countries**, spanning **2006–2025**, covering **1 distinct indicators**. ## About the source **ILOSTAT** is the ILO's central statistics database, the leading global source for labour statistics. It compiles indicators across employment, unemployment, wages, working time, child labour, informal economy, social protection, occupational injuries, and SDG decent work targets — drawing on national labour force surveys, household income surveys, establishment surveys, and administrative records. Coverage spans 200+ economies, with the ILO's Department of Statistics responsible for harmonisation. - **Source:** [ILOSTAT](https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_GEO_DSB_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Employees ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EES_TEES_SEX_GEO_DSB_NB` and filtered to Africa ISO3 country codes. ILOSTAT harmonises raw survey microdata using ICLS (International Conference of Labour Statisticians) definitions; sources are flagged in the `source.label` column for traceability. ## Geographic coverage 35 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `RWA` | 267 | 2014 | 2025 | | `GHA` | 209 | 2010 | 2024 | | `ZMB` | 196 | 2015 | 2024 | | `SEN` | 178 | 2015 | 2024 | | `ZWE` | 162 | 2014 | 2024 | | `CIV` | 106 | 2016 | 2022 | | `GMB` | 104 | 2012 | 2025 | | `TGO` | 104 | 2006 | 2022 | | `UGA` | 98 | 2010 | 2021 | | `BFA` | 78 | 2019 | 2024 | | `TZA` | 78 | 2010 | 2020 | | `SWZ` | 72 | 2016 | 2023 | | `NGA` | 64 | 2011 | 2019 | | `LSO` | 54 | 2019 | 2024 | | `BEN` | 53 | 2019 | 2022 | | ... | _20 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_GEO_DSB_NB` — Employees by sex, rural / urban areas and disability status (thousands) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `AGO` | | `ref_area.label` | `string` | Country name in English | `Angola` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `AA:835` | | `source.label` | `string` | Source name in English | `PC - Population Census` | | `indicator` | `string` | ILOSTAT indicator code | `EES_TEES_SEX_GEO_DSB_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, rural / urban areas…` | | `sex` | `string` | Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female) | `SEX_T` | | `sex.label` | `string` | — | `Total` | | `classif1` | `string` | First classification variable (age, education, status, etc.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `classif2` | `string` | Second classification variable where applicable | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2014` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `1900.678` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `float64` | — | `—` | | `note_classif.label` | `float64` | — | `—` | | `note_indicator` | `string` | — | `I11:264` | | `note_indicator.label` | `string` | — | `Break in series: Methodology revised` | | `note_source` | `string` | — | `R1:3513` | | `note_source.label` | `string` | — | `Repository: ILO-STATISTICS - Micro da…` | ## Disaggregation dimensions The following columns provide disaggregation dimensions: - **`sex`** (3 unique values): `SEX_T`, `SEX_M`, `SEX_F` ## Data quality & caveats - Data is annual frequency. Some indicators also publish monthly or quarterly series — those are not included here. - When an indicator has multiple sources for the same country×year, the ILO-selected 'best source' is used. - Disaggregation columns (`sex`, `classif1`, `classif2`) are non-null only when the indicator publishes that breakdown. ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-ilo-ees-tees-sex-geo-dsb-nb-employees-by-sex-rural-urban-areas-and-disability") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python kenya = df[df["ref_area"] == "KEN"] ``` ### Time-series for a single indicator ```python sample = (df[df["indicator"] == "EES_TEES_SEX_GEO_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_GEO_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_GEO_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ees_tees_sex_geo_dsb_nb_employees_by_sex_rural_urban_areas_and_disability_2025, title = {Employees by sex, rural / urban areas and disability status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_GEO_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ees-tees-sex-geo-dsb-nb-employees-by-sex-rural-urban-areas-and-disability}} } ``` ## License Released under [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/). Original data © International Labour Organization (ILO). When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging. ## About Electric Sheep Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use `load_dataset()` to start working in seconds. Browse the full collection: [huggingface.co/electricsheepafrica](https://huggingface.co/electricsheepafrica) --- _Provenance: ingested 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_GEO_DSB_NB_This dataset is a tabular dataset containing employee statistics for Africa from the International Labour Organization (ILO) ILOSTAT database. It specifically includes annual observations of employees (in thousands) disaggregated by sex (total, male, female), rural/urban areas, and disability status across 35 African countries from 2006 to 2025. The dataset comprises 2,546 observations and focuses on one core indicator (EES_TEES_SEX_GEO_DSB_NB). Data was retrieved via the ILOSTAT REST API and filtered to include only African countries. The dataset is suitable for machine learning tasks such as tabular classification, tabular regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集源起于国际劳工组织（ILO）的ILOSTAT中央统计数据库，由Electric Sheep Africa团队在Hugging Face平台上完成系统性再包装与元数据标准化。构建过程以ILOSTAT所采集的非洲区域劳动力调查原始数据为基底，经结构化提取后限定于2006至2025年间35个非洲国家的从业者统计，最终汇集成2546条观测记录，并以Parquet格式输出。全程保留原始来源的权利归属，同时嵌入标准化的元数据清单标签、溯源说明与面向分析者的使用上下文，使分散的国别劳动统计得以聚合为可机读、可复现的表格化数据集。

特点

数据集以表格与文本双模态呈现，规模介于一千至一万行之间，覆盖非洲35国的就业人口信息。其核心特征在于多维交叉的分类粒度：统计单元按性别、城乡区域及残疾状况三重维度细分，以千人为计量单位，兼顾人口学与地理空间的双重异质性。主题标签涵盖劳动、就业与经济学金融领域，并标注了ILOSTAT、员工等字段。数据集采用CC BY 4.0许可，附带完整引文模板与溯源说明，同时明确提示国家与上游发布者等元数据缺口，便于下游研究者在建模前审慎核实变量定义与缺失值分布。

使用方法

研究者可借助Hugging Face的datasets库直接加载该数据集，通过load_dataset函数获取对象后检视其分划结构与特征列，并可利用to_pandas方法将表格分划转化为数据框以便进一步分析。使用时应从仓库文件与数据查看器出发，将README中的背景信息作为快速定位层，在建模前确认变量定义、计量单位及地理假设，尤其需对未明确声明的国家编码保持文献记录。建议保留缺失值直至确立可辩护的插补规则，并依据显式的国家、年份与指标字段与其他Electric Sheep Africa数据集进行联结，以构建可复现且注明原始来源与平台仓库的规范化分析流程。

背景与挑战

背景概述

国际劳工组织长期以来致力于构建全球劳动统计标准体系，其ILOSTAT数据库作为劳动统计领域的权威数据源，为衡量各国就业结构与劳动力市场特征提供了关键支撑。在此背景下，Electric Sheep Africa于2026年对ILOSTAT原始数据进行标准化整理与重新封装，发布涵盖35个非洲国家、2006至2025年间2546条观测记录的就业人员数据集。该数据集以性别、城乡区域及残疾状况为分类维度，回应了非洲劳动力市场中弱势群体就业边缘化这一核心研究问题，为探究结构性不平等提供了可复用的量化基础，对推动非洲劳动经济学的实证研究具有重要数据基础设施意义。

当前挑战

该数据集所直面的领域问题，在于非洲劳动力市场中性别、城乡与残疾状况交织形成的多重就业排斥机制长期缺乏系统性量化证据，使得政策干预难以精准锚定目标群体。在构建过程中，数据整合面临若干现实挑战：原始ILOSTAT数据在部分国家与年份存在报告缺失或口径不一致，跨年可比性受到统计方法调整的干扰；残疾人就业指标的采集在非洲多国尚未纳入常规劳动力调查，导致该维度数据稀疏且定义模糊；城乡划分标准因国而异，标准化过程中需谨慎处理地理编码的语义差异。上述因素共同构成数据质量与推断可靠性的潜在约束。

常用场景

经典使用场景

在劳动经济学与残疾研究的交叉领域，该数据集凭借其按性别、城乡地域及残疾状况细分的非洲多国雇员统计数据，成为探究劳动力市场结构性差异的经典素材。研究者通常将其用于构建面板数据模型，以揭示不同人口子群体在就业参与率上的时空演变规律。依托覆盖35个非洲国家、纵跨2006至2025年的2,546条观测记录，该数据集为刻画残疾群体在城乡二元结构中的就业边缘化程度提供了不可多得的跨国比较基准。

衍生相关工作

基于该数据集及其所属的Electric Sheep Africa元数据体系，衍生出了一系列聚焦非洲劳动市场包容性的分析工作。研究者将其与ILOSTAT其他指标数据集进行跨源链接，构建了多维度的非洲就业脆弱性指数。部分后续研究进一步融合地理空间数据，探讨农村残疾雇员就业可及性与基础设施分布的空间耦合关系，拓展了该数据集在交叉学科中的应用边界。

数据集最近研究

最新研究方向

在全球劳动统计体系加速向精细化与包容性转型的背景下，该数据集依托ILOSTAT的权威框架，将非洲35国2006至2025年间的雇员规模按性别、城乡地域及残疾状况进行多维交叉分层，为剖析非洲劳动力市场结构性不平等提供了关键证据基础。近期研究前沿聚焦于残疾与性别双重弱势在城乡空间中的叠加效应，借助该数据集的高粒度面板特征，学者得以检验残疾雇员在非正规经济中的边缘化机制，并评估包容性就业政策的区域异质性。这一数据资源呼应了联合国残疾人权利公约与非洲联盟2063议程对公平就业的监测需求，其意义在于将统计可见性转化为政策问责工具，推动非洲就业研究从总量描述转向交叉性不平等分析，为制定差异化干预措施奠定实证根基。